Efficient Turkish Text Classification Approach for Crisis Management Systems

In this paper, an effective tweet classification system that fully supports the Turkish language has been developed. The proposed can be used for mining (classifying) recently published and publicly available tweets to find crisis’s most related useful gain situational awareness, which help in taking correct responses order prevent or at least decrease effect of such situations. A deep study was carried out improve optimize system. more detail, some intensive experiments were performed investigate performance well-known machine learning algorithms, i.e., K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes (NB) when text (tweets) classification. Then, performances ensemble systems studied algorithms Random Forest (RF), AdaBoost Classifier (AdaBoost), GradientBoosting (GBC) have also observed. As shown experimental evaluation analysis, approach stability, robustness, achieve quite good processing language. classifier compared with two state-of-the-art approaches, "Empirical" “Turkish Deep ".